


import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase			=				logging.get_logger(__name__)
def      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	):


		"""simple docstring"""




		lowercase__				= original_name.split('''.'''	)[0]
		lowercase__				= key.split('''.'''	)
		lowercase__				= int(key_list[key_list.index(SCREAMING_SNAKE_CASE	) - 2]	)
		lowercase__				= int(key_list[key_list.index(SCREAMING_SNAKE_CASE	) - 1]	)
		lowercase__				= orig_block_num - offset

		lowercase__				= key.replace(f'{orig_block_num}.{layer_num}.{original_name}'		,							f'block.{new_block_num}.{layer_num}.{new_name}'	)
		return key
def      _a					( SCREAMING_SNAKE_CASE	):


		"""simple docstring"""




		lowercase__				= OrderedDict()
		lowercase__	,						lowercase__				= 0, 0
		for key, value in state_dict.items():
				if key.startswith('''network'''	):
						lowercase__				= key.replace('''network'''		,							'''poolformer.encoder'''	)
				if "proj" in key:
						# Works for the first embedding as well as the internal embedding layers
						if key.endswith('''bias'''	) and "patch_embed" not in key:
								patch_emb_offset += 1
						lowercase__				= key[: key.find('''proj'''	)]
						lowercase__				= key.replace(SCREAMING_SNAKE_CASE		,							f'patch_embeddings.{total_embed_found}.'	)
						lowercase__				= key.replace('''proj'''		,							'''projection'''	)
						if key.endswith('''bias'''	):
								total_embed_found += 1
				if "patch_embeddings" in key:
						lowercase__				= '''poolformer.encoder.''' + key
				if "mlp.fc1" in key:
						lowercase__				= replace_key_with_offset(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							'''mlp.fc1'''		,							'''output.conv1'''	)
				if "mlp.fc2" in key:
						lowercase__				= replace_key_with_offset(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							'''mlp.fc2'''		,							'''output.conv2'''	)
				if "norm1" in key:
						lowercase__				= replace_key_with_offset(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							'''norm1'''		,							'''before_norm'''	)
				if "norm2" in key:
						lowercase__				= replace_key_with_offset(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							'''norm2'''		,							'''after_norm'''	)
				if "layer_scale_1" in key:
						lowercase__				= replace_key_with_offset(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							'''layer_scale_1'''		,							'''layer_scale_1'''	)
				if "layer_scale_2" in key:
						lowercase__				= replace_key_with_offset(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							'''layer_scale_2'''		,							'''layer_scale_2'''	)
				if "head" in key:
						lowercase__				= key.replace('''head'''		,							'''classifier'''	)
				lowercase__				= value
		return new_state_dict
def      _a					( ):


		"""simple docstring"""




		lowercase__				= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
		lowercase__				= Image.open(requests.get(SCREAMING_SNAKE_CASE		,							stream=SCREAMING_SNAKE_CASE	).raw	)

		return image
@torch.no_grad()
def      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	):


		"""simple docstring"""




		lowercase__				= PoolFormerConfig()

		# set attributes based on model_name
		lowercase__				= '''huggingface/label-files'''
		lowercase__				= model_name[-3:]
		lowercase__				= 10_00
		lowercase__				= '''imagenet-1k-id2label.json'''
		lowercase__				= (1, 10_00)

		# set config attributes
		lowercase__				= json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							repo_type='''dataset'''	)		,							'''r'''	)	)
		lowercase__				= {int(SCREAMING_SNAKE_CASE	): v for k, v in idalabel.items()}
		lowercase__				= idalabel
		lowercase__				= {v: k for k, v in idalabel.items()}
		if size == "s12":
				lowercase__				= [2, 2, 6, 2]
				lowercase__				= [64, 1_28, 3_20, 5_12]
				lowercase__				= 4.0
				lowercase__				= 0.9
		elif size == "s24":
				lowercase__				= [4, 4, 12, 4]
				lowercase__				= [64, 1_28, 3_20, 5_12]
				lowercase__				= 4.0
				lowercase__				= 0.9
		elif size == "s36":
				lowercase__				= [6, 6, 18, 6]
				lowercase__				= [64, 1_28, 3_20, 5_12]
				lowercase__				= 4.0
				lowercase__				= 1E-6
				lowercase__				= 0.9
		elif size == "m36":
				lowercase__				= [6, 6, 18, 6]
				lowercase__				= [96, 1_92, 3_84, 7_68]
				lowercase__				= 4.0
				lowercase__				= 1E-6
				lowercase__				= 0.95
		elif size == "m48":
				lowercase__				= [8, 8, 24, 8]
				lowercase__				= [96, 1_92, 3_84, 7_68]
				lowercase__				= 4.0
				lowercase__				= 1E-6
				lowercase__				= 0.95
		else:
				raise ValueError(f'Size {size} not supported'	)

		# load image processor
		lowercase__				= PoolFormerImageProcessor(crop_pct=SCREAMING_SNAKE_CASE	)

		# Prepare image
		lowercase__				= prepare_img()
		lowercase__				= image_processor(images=SCREAMING_SNAKE_CASE		,							return_tensors='''pt'''	).pixel_values

		logger.info(f'Converting model {model_name}...'	)

		# load original state dict
		lowercase__				= torch.load(SCREAMING_SNAKE_CASE		,							map_location=torch.device('''cpu'''	)	)

		# rename keys
		lowercase__				= rename_keys(SCREAMING_SNAKE_CASE	)

		# create HuggingFace model and load state dict
		lowercase__				= PoolFormerForImageClassification(SCREAMING_SNAKE_CASE	)
		model.load_state_dict(SCREAMING_SNAKE_CASE	)
		model.eval()

		# Define image processor
		lowercase__				= PoolFormerImageProcessor(crop_pct=SCREAMING_SNAKE_CASE	)
		lowercase__				= image_processor(images=prepare_img()		,							return_tensors='''pt'''	).pixel_values

		# forward pass
		lowercase__				= model(SCREAMING_SNAKE_CASE	)
		lowercase__				= outputs.logits

		# define expected logit slices for different models
		if size == "s12":
				lowercase__				= torch.tensor([-0.3_045, -0.6_758, -0.4_869]	)
		elif size == "s24":
				lowercase__				= torch.tensor([0.4_402, -0.1_374, -0.8_045]	)
		elif size == "s36":
				lowercase__				= torch.tensor([-0.6_080, -0.5_133, -0.5_898]	)
		elif size == "m36":
				lowercase__				= torch.tensor([0.3_952, 0.2_263, -1.2_668]	)
		elif size == "m48":
				lowercase__				= torch.tensor([0.1_167, -0.0_656, -0.3_423]	)
		else:
				raise ValueError(f'Size {size} not supported'	)

		# verify logits
		assert logits.shape == expected_shape
		assert torch.allclose(logits[0, :3]		,							SCREAMING_SNAKE_CASE		,							atol=1E-2	)

		# finally, save model and image processor
		logger.info(f'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'	)
		Path(SCREAMING_SNAKE_CASE	).mkdir(exist_ok=SCREAMING_SNAKE_CASE	)
		model.save_pretrained(SCREAMING_SNAKE_CASE	)
		print(f'Saving image processor to {pytorch_dump_folder_path}'	)
		image_processor.save_pretrained(SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
			lowerCAmelCase			=				argparse.ArgumentParser()

			parser.add_argument(
			    '--model_name',
			    default='poolformer_s12',
			    type=str,
			    help='Name of the model you\'d like to convert.',
			)
			parser.add_argument(
			    '--checkpoint_path', default=None, type=str, help='Path to the original PyTorch checkpoint (.pth file).'
			)
			parser.add_argument(
			    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
			)
			lowerCAmelCase			=				parser.parse_args()
			convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)






'''simple docstring'''


import os


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
UpperCAmelCase_						:  List[Any]				  =						'.'


if __name__ == "__main__":
				UpperCAmelCase_						:  Any				  =						os.path.join(REPO_PATH, 'utils/documentation_tests.txt')
				UpperCAmelCase_						:  Dict				  =						[]
				UpperCAmelCase_						:  Union[str, Any]				  =						[]
				with open(doctest_file_path) as fp:
								for line in fp:
												UpperCAmelCase_						:  int				  =						line.strip()
												UpperCAmelCase_						:  str				  =						os.path.join(REPO_PATH, line)
												if not (os.path.isfile(path) or os.path.isdir(path)):
																non_existent_paths.append(line)
												all_paths.append(path)
				if len(non_existent_paths) > 0:
								UpperCAmelCase_						:  int				  =						'\n'.join(non_existent_paths)
								raise ValueError(F"`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}")
				if all_paths != sorted(all_paths):
								raise ValueError('Files in `utils/documentation_tests.txt` are not in alphabetical order.')




